7 free Google AI courses: Master LLMs, ML, and more in under an hour

Artificial Intelligence is quickly dominating sectors. The scare of job losses is visible, yet as businesses globally are racing to automate functions, professionals can also gain an advantage by being current.

The technology sector is in the middle of a shift with firms of all sizes worldwide implementing AI technologies. And the way to remain relevant in times like these is by upskilling oneself. Although AI technologies are replacing a lot of work, nothing is going to waste; there are still several new jobs being formed.

 

If you are looking to upskill yourself, then no platform is as great as the Internet. There are thousands of courses that are available for free and can provide you with the skills that can leave an impact on your resume. Here's a list of some of the free courses provided by Google on AI.

Introduction to Large Language Models (LLM)

An LLM is an artificial intelligence model that has been trained on huge quantities of datasets to generate human-like written responses. ChatGPT is one such LLM. Although everyone might know what all ChatGPT can accomplish, not many might possess the expertise of how an LLM works. This is a beginner course that provides an overview of what LLMs are, their applications, and how to utilize prompt tuning to enhance LLM performance. The course explores Google tools that you can use to build your own Gen AI apps. If all you want to do is learn the basics and create some Gen AI apps, this is the course for you. Introduction to Image Generation

Ever been curious about how an AI model creates an image?

Well, if you feel the need to satisfy your curiosity, this little course will get you familiar with diffusion models, a family of machine learning models that have been driving the AI image generation models. The course takes you through the theory aspect of diffusion models and how to train and deploy them in Vertex AI, Google's single platform to develop ML and AI models. The course further provides an editable badge upon completion which can complement your professional profile leading to increased career prospects later on. Encoder-Decoder Architecture This is a 30-minute course that provides an overview of the encoder-decoder architecture, a machine learning architecture for sequence-to-sequence tasks.

The major tasks are primarily machine translation, text summarization, and question answering.

Students will have an opportunity to know the primary building blocks of this architecture and understand how to train and serve these models. In addition, they will also have the opportunity to code in TensorFlow, Google's open-source software library for ML and deep learning use cases. Upon graduation, learners will receive a shareable badge to demonstrate the user's expertise. Introduction to Generative AI  This can be an excellent intro if you are new to generative AI. This is a microlearning course that describes what generative AI is, how it is utilized, and why it differs from conventional ML models.

The course covers various Google Tools to enable one to create their own Gen AI apps.

The around 45-minute course provides shareable badges upon completion. The badge can be seen in a user's profile and can even be shared with their social network. Attention Mechanism Attention mechanism is a deep learning technique by which AI models can concentrate on individual or most important sections of their inputs during information processing, rather than treating everything equally. This 45-minute course provides insights into this strong technique enabling neural networks to concentrate on meaningful sections of an input sequence.

Students will learn about how attention functions, and how to leverage it to enhance the accuracy of a range of machine learning applications such as text summarization, machine translation, question answering, etc.

Shareable badges are also provided with the course. Transformer Models and BERT This course provides an overview of Bidirectional Encoder Representations from Transformer (BERT) model and transformer architecture. Students will learn about how the self-attention mechanism functions and drives BERT models. The course also explains how BERT can be applied to tasks like question answering, text classification, and natural language interface. The course should take approximately 45 minutes to complete, and it is also equipped with a shareable skills badge. Create Image Captioning Models This 30-minute course will instruct students on how to develop an image captioning model using deep learning.

The course discusses various parts of an image captioning model, including encoder and decoder, and even training a model and testing a model.

Google asserts that at the end of this course, a user will learn to develop their own image captioning models and use them to generate captions on images.

This course has an assignable badge.

These micro-learning courses provided by Google are a series of video tutorials along with quizzes that intend to teach the learners some of the most important things about AI. It should be mentioned that while signing up, the majority of such courses permit users to access study materials such as videos and documents for no cost. If any of the courses include labs, however, students are recommended to buy individual subscriptions or credits so that they can take maximum benefit from the labs. In order to receive the badge, students would need to finish all compulsory activities in a course